Incorporating Cluster Information into Multispectral Image Edge Detection
3.2 Cluster analysis
Each pixel in the image can be plotted as vector in a multidimensional space. The cluster analysis transforms the original remote sensing images into a set of linear chromatic. An appropriate chromatic transform is chosen for each pixel. In this paper, k-mean clustering method [4] is chosen. The algorithm is described in the following
-
Allocate each pattern vector to a group corresponding to the nearest group mean.
- Calculate the new cluster centers as the average of all the pattern vectors in each cluster group.
- It they are al the same as the old cluster centers, STOP. Otherwise go to step 1.
The cluster analysis provides the average vectors or the center (Ci, i = 1, 2 3, … , n) of each cluster and these vectors are used to calculate the appropriate projection vector.
3.3 Projection of gradient vectors
Suppose two regions R1 and R2 belong to the cluster C
1 and C
2. The centers of these two clusters are V
1 and V
2. The gradient vector of a pixel around the boundary between R
1 and R
2 is likely to have the same direction of V
1 -V
2, Then V
1 -V
2 is a good choice of projection vector for edge between R
1 and R
2. For a given image, if n cluster are found, there are n(n-1)/2 possible projection vectors. If the standard projection vector, the number of possible projection vectors is then (n(n-1)/2+1. For each pixel of the image, its gradient vector is projected to all these (n(n-1)/2+1 projection vectors and the maximum value is adopted.
4. Experimental results
In this paper, the proposed algorithm has been implemented and applied to several JERS-1/OPS-VNIR images, band 1,2 and 3. The gradient information in each spectral band is calculated using Sobel operator. The number of cluster allows for each image is 4. The original "color" JERS-1/OPS-VNIR image is shown in Figure 3(a). Figure 3(b) shows the segmented image resulting from the k-mean clustering algorithm. Figure 3(c) shows the gradient image calculated on the monochrome version of figure (3a). Comparing the Figure 3(d), which is the gradient image obtained by the proposed algorithm, it is obvious that more significant edges can be detected by this algorithm.
(a)
(b)
(c)
(d)
Figure 3: Experimental result. (a) Original JERS-1/OPS-VNIR (color) image. (b) Segmented image of a (a) into 4 cluster. (c) Gradient image calculated on monochrome version of (a). (d) Gradient image obtained by the proposed method.
5. Conclusion
The advantage of the proposed algorithm is that global information of a given multispectral remote sensing image is exploited to guide the local edge detector. Use of each spectral information is enabled by having multiple projection vectors. So this method can increase the efficiency of the edge detection, but some problems related this scheme is the computation complexity caused by the clustering process. This problem can be partially solved by sub-sampling the original image.
Acknowledgement
The authors wish to thank the national Research Council of Thailand (NRCT) for providing the satellite image data.
Reference
- A.K. Jain, Fundamentals of Digital Image Processing. Prentice-Hall, Englewood Cliffs, 987.
- R.C. Gonzalez and R.E. Woods, Digital Image Processing, Addison -Westley, 1992.
- J.T. Allen and T. Hunsberger, "Comparing color Edge Dectection and Segmentation Methods, " In IEEE SOUTH EASTCON '89, pp. 722-758, 1989.
- A. Low; Introductory Computer vision and Image Processing. McGraw-Hill, 1991.
- R. Nevatia, "A color Edge Detecor and Its Use in Scene Segmentation, " IEEE Trans. Syst. Man Cybern., vol 7, pp. 820-826, 1997.
